Artificial life: the quest for a new creation
Artificial life: the quest for a new creation
C4.5: programs for machine learning
C4.5: programs for machine learning
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering Temporal Rules from Temporally Ordered Data
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
RFCT: An Association-Based Causality Miner
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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Observing the world and finding trends and relations among the variables of interest is an important and common learning activity. In this paper we apply TETRAD, a program that uses Bayesian networks to discover causal rules, and C4.5, which creates decision trees, to the problem of discovering relations among a set of variables in the controlled environment of an Artificial Life simulator. All data in this environment are generated by a single entity over time. The rules in the domain are known, so we are able to assess the effectiveness of each method. The agent's sensings of its environment and its own actions are saved in data records over time. We first compare TETRAD and C4.5 in discovering the relations between variables in a single record. We next attempt to find temporal relations among the variables of consecutive records. Since both these programs disregard the passage of time among the records, we introduce the flattening operation as a way to span time and bring the variables of interest together in a new single record. We observe that flattening allows C4.5 to discover relations among variables over time, while it does not improve TETRAD's output.